feature op• 데이터 종류: voxel → voxel
• 호출: import fullseye as fs; fs.ledger.vol_local_std(vol, size=5)(구현을 직접 호출하려면 import volops; volops.vol_local_std(vol, size=5), 원장에서 가져오려면 ops3d.get("vol_local_std"))
정육면체 창 안의 국소 표준편차(불편추정).
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
The 3-D counterpart of the registry op `local_std` (HALCON's
`deviation_image`). The variance is taken **after subtracting the global
mean** — `E[x^2] - E[x]^2` loses every significant digit on a volume whose
values sit far from zero, and the leftover shows up as a fake texture on a
uniform block. It is then unbiased twice: `n/(n-1)` on the variance and
`c4(n) on the square root, so the estimate of sigma` itself (not of
`sigma^2`) is unbiased.
*size* is the edge of the cubic window in voxels (odd, >= 3), so
`n = size**3` voxels enter each estimate. **The relative standard error of
a single voxel's estimate is `1/sqrt(2(n-1))`** — 3.1% for a 5x5x5 window,
1.1% for 9x9x9. Quote that figure whenever a noise or roughness number is
read off this volume.
Returns a `(D, H, W)` float64 volume in the input's own units (it is *not*
normalised, so values from different scans compare directly).
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• ct_porosity_and_fibre_morphometry — py -3.11 examples_3d/ct_porosity_and_fibre_morphometry.py
voxel 를 입력으로 받는 것)voxel_to_mips · voxel_to_mesh · signed_distance_field · to_points · sobel3d · hessian3d · curvature_maps · edt_jfa
feature)sobel3d · hessian3d · curvature_maps · edt_jfa · vol_frangi · vol_local_thickness · vol_orientation_coherence · vol_euler_number
*Provenance: volops.py — 3D 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.